Job Title: Data Engineer
Duration: 12+ Months with possible extension
Location: Westmont, IL
Required Qualifications
• 7+ years of experience in data engineering and Big Data development, including multiple large, complex project deliveries.
• Strong hands-on experience with cloud data platforms on Azure or AWS, including services such as Databricks, Azure Data Factory, Synapse, AWS Glue, or EMR.
• Advanced proficiency in Python and SQL, with deep expertise in PySpark/Spark for distributed data processing at scale.
• Proven expertise in data modeling and data architecture, including advanced database design and optimization across SQL (e.g., Azure SQL, PostgreSQL) and NoSQL (e.g., Cosmos DB, MongoDB) stores.
• Experience designing and building API services and integrating data platforms with downstream applications.
• Hands-on experience with LLM-driven workflows, RAG architectures, or Model Context Protocol (MCP) integrations — or demonstrable experience preparing enterprise data for AI/agentic consumption.
• Track record of leading workstreams and driving technical decisions across teams as a senior individual contributor.
• Availability to work on a contract basis with structured deliverables, and to complete thorough documentation and knowledge transfer.
Preferred Qualifications
• Experience with streaming technologies such as Kafka, Spark Streaming, or Azure Event Hubs.
• Familiarity with vector databases (e.g., Pinecone, Weaviate) and knowledge/graph databases (e.g., Neo4j).
• Experience with orchestration frameworks for LLM applications (e.g., LangChain, LlamaIndex) and advanced prompt engineering.
• Working knowledge of DevOps practices: CI/CD pipelines, infrastructure as code (Terraform), and containerization (Kubernetes).
• Exposure to commercial real estate, CRM, or transaction/deal-pipeline data domains.
• Experience with data governance frameworks and compliance standards (GDPR, CCPA).
Key attributes
Problem-Solving Skills: Breaks down complex, ambiguous data challenges into pragmatic, well-architected solutions.
Ownership & Drive: Takes full accountability for deliverables from design through production handoff, without needing close supervision.
Technical Influence: Drives sound technical decisions across teams through evidence, prototypes, and clear architectural reasoning.
Communication: Explains complex technical concepts and trade-offs clearly to engineers, product partners, and stakeholders.
Collaboration: Works effectively with JLL engineers, product owners, and cross-functional teams, prioritizing continuity and knowledge sharing.
Adaptability & Speed: Delivers high-quality results quickly within a fixed engagement window, adjusting to evolving priorities.